Abrupt transitions in collaborative social networks

TL;DR

This paper introduces an early warning method based on network properties to detect abrupt changes in social systems, demonstrated through analysis of three collaborative social networks and a new clique growth model.

Contribution

It develops a novel early warning approach for social network transitions and proposes a clique growth model that explains their universal properties.

Findings

Abrupt transitions can signal major social events.

Network properties serve as effective early warning indicators.

Real networks belong to a new universality class described by the Gumbel distribution.

Abstract

Despite the wide use of networks as a versatile tool for exploring complex social systems, little is known about how to detect and forecast abrupt changes in social systems. In this report, we develop an early warning approach based on network properties to detect such changes. By analysing three collaborative social networks---one co-stardom, one patent and one scientific collaborative network, we discover that abrupt transitions inherent in these networks can serve as a good early warning signal, indicating, respectively, the dissolution of the Soviet Union, the emergence of the "soft matter" research field, and the merging of two scientific communities. We then develop a clique growth model that explains the universal properties of these real networks and find that they belong to a new universality class, described by the Gumbel distribution.